Overview

Dataset statistics

Number of variables12
Number of observations163
Missing cells325
Missing cells (%)16.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.4 KiB
Average record size in memory96.8 B

Variable types

Numeric12

Alerts

ICFES_ANTIGUO is highly overall correlated with BIOLOGIA and 9 other fieldsHigh correlation
BIOLOGIA is highly overall correlated with ICFES_ANTIGUO and 8 other fieldsHigh correlation
MATEMATICA is highly overall correlated with ICFES_ANTIGUO and 8 other fieldsHigh correlation
FILOSOFIA is highly overall correlated with ICFES_ANTIGUO and 8 other fieldsHigh correlation
FISICA is highly overall correlated with ICFES_ANTIGUO and 8 other fieldsHigh correlation
HISTORIA is highly overall correlated with ICFES_ANTIGUO and 8 other fieldsHigh correlation
QUIMICA is highly overall correlated with ICFES_ANTIGUO and 8 other fieldsHigh correlation
LENGUAJE is highly overall correlated with ICFES_ANTIGUO and 8 other fieldsHigh correlation
GEOGRAFIA is highly overall correlated with ICFES_ANTIGUO and 8 other fieldsHigh correlation
IDIOMA is highly overall correlated with ICFES_ANTIGUO and 8 other fieldsHigh correlation
INTERDISCIPLINAR is highly overall correlated with ICFES_ANTIGUOHigh correlation
ICFES_ANTIGUO has 22 (13.5%) missing valuesMissing
BIOLOGIA has 19 (11.7%) missing valuesMissing
MATEMATICA has 19 (11.7%) missing valuesMissing
FILOSOFIA has 18 (11.0%) missing valuesMissing
FISICA has 18 (11.0%) missing valuesMissing
HISTORIA has 18 (11.0%) missing valuesMissing
QUIMICA has 18 (11.0%) missing valuesMissing
LENGUAJE has 19 (11.7%) missing valuesMissing
GEOGRAFIA has 18 (11.0%) missing valuesMissing
IDIOMA has 19 (11.7%) missing valuesMissing
INTERDISCIPLINAR has 137 (84.0%) missing valuesMissing
ID_ESTUDIANTE has unique valuesUnique
INTERDISCIPLINAR has 11 (6.7%) zerosZeros

Reproduction

Analysis started2023-09-15 01:03:12.953437
Analysis finished2023-09-15 01:03:30.465504
Duration17.51 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

ID_ESTUDIANTE
Real number (ℝ)

Distinct163
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2215167.7
Minimum2185175
Maximum2237170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-09-14T20:03:30.574597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2185175
5-th percentile2185326.4
Q12204975.5
median2220180
Q32230219.5
95-th percentile2235576.9
Maximum2237170
Range51995
Interquartile range (IQR)25244

Descriptive statistics

Standard deviation16264.874
Coefficient of variation (CV)0.0073425026
Kurtosis-0.91911942
Mean2215167.7
Median Absolute Deviation (MAD)11198
Skewness-0.58110456
Sum3.6107233 × 108
Variance2.6454614 × 108
MonotonicityNot monotonic
2023-09-14T20:03:30.763267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2236077 1
 
0.6%
2231513 1
 
0.6%
2221978 1
 
0.6%
2231709 1
 
0.6%
2235011 1
 
0.6%
2235012 1
 
0.6%
2235053 1
 
0.6%
2216051 1
 
0.6%
2225180 1
 
0.6%
2216138 1
 
0.6%
Other values (153) 153
93.9%
ValueCountFrequency (%)
2185175 1
0.6%
2185181 1
0.6%
2185221 1
0.6%
2185265 1
0.6%
2185297 1
0.6%
2185298 1
0.6%
2185299 1
0.6%
2185300 1
0.6%
2185320 1
0.6%
2185384 1
0.6%
ValueCountFrequency (%)
2237170 1
0.6%
2236497 1
0.6%
2236496 1
0.6%
2236229 1
0.6%
2236079 1
0.6%
2236078 1
0.6%
2236077 1
0.6%
2235630 1
0.6%
2235577 1
0.6%
2235576 1
0.6%

ICFES_ANTIGUO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct95
Distinct (%)67.4%
Missing22
Missing (%)13.5%
Infinite0
Infinite (%)0.0%
Mean264.59574
Minimum0
Maximum386
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-09-14T20:03:30.939516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q1245
median280
Q3315
95-th percentile349
Maximum386
Range386
Interquartile range (IQR)70

Descriptive statistics

Standard deviation87.537092
Coefficient of variation (CV)0.33083333
Kurtosis3.928686
Mean264.59574
Median Absolute Deviation (MAD)35
Skewness-1.9823079
Sum37308
Variance7662.7426
MonotonicityNot monotonic
2023-09-14T20:03:31.134134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10
 
6.1%
280 4
 
2.5%
315 4
 
2.5%
254 3
 
1.8%
245 3
 
1.8%
278 3
 
1.8%
232 3
 
1.8%
320 3
 
1.8%
307 3
 
1.8%
268 2
 
1.2%
Other values (85) 103
63.2%
(Missing) 22
 
13.5%
ValueCountFrequency (%)
0 1
 
0.6%
1 10
6.1%
188 1
 
0.6%
196 1
 
0.6%
205 1
 
0.6%
207 2
 
1.2%
211 1
 
0.6%
218 1
 
0.6%
220 1
 
0.6%
225 1
 
0.6%
ValueCountFrequency (%)
386 1
0.6%
383 1
0.6%
381 1
0.6%
378 1
0.6%
368 1
0.6%
363 1
0.6%
356 1
0.6%
349 2
1.2%
345 1
0.6%
344 1
0.6%

BIOLOGIA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)29.9%
Missing19
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean51.673819
Minimum1
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-09-14T20:03:31.313009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q147
median54
Q362
95-th percentile71
Maximum76
Range75
Interquartile range (IQR)15

Descriptive statistics

Standard deviation16.81104
Coefficient of variation (CV)0.32532992
Kurtosis3.3278208
Mean51.673819
Median Absolute Deviation (MAD)7
Skewness-1.7260438
Sum7441.03
Variance282.61105
MonotonicityNot monotonic
2023-09-14T20:03:31.490087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 10
 
6.1%
58 8
 
4.9%
53 7
 
4.3%
51 7
 
4.3%
56 6
 
3.7%
52 6
 
3.7%
60 6
 
3.7%
66 6
 
3.7%
45 5
 
3.1%
55 5
 
3.1%
Other values (33) 78
47.9%
(Missing) 19
 
11.7%
ValueCountFrequency (%)
1 10
6.1%
31 1
 
0.6%
34 1
 
0.6%
36 2
 
1.2%
37 1
 
0.6%
38 1
 
0.6%
39 3
 
1.8%
40 2
 
1.2%
41 1
 
0.6%
42 1
 
0.6%
ValueCountFrequency (%)
76 2
 
1.2%
75 1
 
0.6%
72 3
1.8%
71 3
1.8%
70 3
1.8%
69 3
1.8%
68 3
1.8%
67 2
 
1.2%
66 6
3.7%
65 3
1.8%

MATEMATICA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)29.2%
Missing19
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean54.216111
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-09-14T20:03:31.667493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q150
median58
Q363
95-th percentile73.85
Maximum100
Range99
Interquartile range (IQR)13

Descriptive statistics

Standard deviation17.38165
Coefficient of variation (CV)0.32059934
Kurtosis3.9817054
Mean54.216111
Median Absolute Deviation (MAD)6.5
Skewness-1.7393765
Sum7807.12
Variance302.12174
MonotonicityNot monotonic
2023-09-14T20:03:31.839554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 10
 
6.1%
61 10
 
6.1%
60 8
 
4.9%
53 8
 
4.9%
58 8
 
4.9%
63 7
 
4.3%
54 6
 
3.7%
50 6
 
3.7%
66 6
 
3.7%
51 5
 
3.1%
Other values (32) 70
42.9%
(Missing) 19
 
11.7%
ValueCountFrequency (%)
1 10
6.1%
33 1
 
0.6%
35 1
 
0.6%
39 1
 
0.6%
40 1
 
0.6%
41 2
 
1.2%
42.12 1
 
0.6%
43 1
 
0.6%
44 3
 
1.8%
45 1
 
0.6%
ValueCountFrequency (%)
100 1
 
0.6%
82 1
 
0.6%
79 1
 
0.6%
76 1
 
0.6%
75 1
 
0.6%
74 3
1.8%
73 1
 
0.6%
72 4
2.5%
70 2
1.2%
69 3
1.8%

FILOSOFIA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)31.7%
Missing18
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean54.452207
Minimum1
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-09-14T20:03:32.007840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q151
median58
Q364
95-th percentile72.8
Maximum83
Range82
Interquartile range (IQR)13

Descriptive statistics

Standard deviation17.4714
Coefficient of variation (CV)0.32085752
Kurtosis3.6596279
Mean54.452207
Median Absolute Deviation (MAD)7
Skewness-1.8265131
Sum7895.57
Variance305.24982
MonotonicityNot monotonic
2023-09-14T20:03:32.180520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1 10
 
6.1%
63 9
 
5.5%
58 8
 
4.9%
57 7
 
4.3%
54 7
 
4.3%
55 6
 
3.7%
62 5
 
3.1%
70 5
 
3.1%
66 5
 
3.1%
59 5
 
3.1%
Other values (36) 78
47.9%
(Missing) 18
 
11.0%
ValueCountFrequency (%)
1 10
6.1%
31 1
 
0.6%
36 1
 
0.6%
37 1
 
0.6%
38 1
 
0.6%
39 1
 
0.6%
40 2
 
1.2%
41 4
 
2.5%
42 1
 
0.6%
44 1
 
0.6%
ValueCountFrequency (%)
83 1
 
0.6%
81 1
 
0.6%
80 1
 
0.6%
79 1
 
0.6%
78 1
 
0.6%
75 2
 
1.2%
73 1
 
0.6%
72 3
1.8%
71 2
 
1.2%
70 5
3.1%

FISICA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)27.6%
Missing18
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean53.815172
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-09-14T20:03:32.346393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q150
median57
Q363
95-th percentile72
Maximum100
Range99
Interquartile range (IQR)13

Descriptive statistics

Standard deviation17.343364
Coefficient of variation (CV)0.32227647
Kurtosis3.8155992
Mean53.815172
Median Absolute Deviation (MAD)6
Skewness-1.7345002
Sum7803.2
Variance300.79227
MonotonicityNot monotonic
2023-09-14T20:03:32.516923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
60 10
 
6.1%
1 10
 
6.1%
53 9
 
5.5%
58 8
 
4.9%
61 7
 
4.3%
50 7
 
4.3%
47 6
 
3.7%
63 6
 
3.7%
59 6
 
3.7%
55 5
 
3.1%
Other values (30) 71
43.6%
(Missing) 18
 
11.0%
ValueCountFrequency (%)
1 10
6.1%
30 1
 
0.6%
32 1
 
0.6%
35 1
 
0.6%
36 2
 
1.2%
37 1
 
0.6%
41 3
 
1.8%
44 2
 
1.2%
45 1
 
0.6%
47 6
3.7%
ValueCountFrequency (%)
100 1
 
0.6%
79 1
 
0.6%
76 1
 
0.6%
74 2
 
1.2%
72 5
3.1%
71 3
1.8%
70 2
 
1.2%
69 3
1.8%
68 3
1.8%
67 3
1.8%

HISTORIA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct48
Distinct (%)33.1%
Missing18
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean51.582552
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-09-14T20:03:32.687107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q144
median56
Q363
95-th percentile72.8
Maximum81
Range80
Interquartile range (IQR)19

Descriptive statistics

Standard deviation17.682837
Coefficient of variation (CV)0.34280656
Kurtosis2.2459668
Mean51.582552
Median Absolute Deviation (MAD)10
Skewness-1.4334964
Sum7479.47
Variance312.68273
MonotonicityNot monotonic
2023-09-14T20:03:32.887706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1 10
 
6.1%
66 7
 
4.3%
44 7
 
4.3%
59 7
 
4.3%
58 6
 
3.7%
63 6
 
3.7%
57 6
 
3.7%
67 5
 
3.1%
42 5
 
3.1%
43 5
 
3.1%
Other values (38) 81
49.7%
(Missing) 18
 
11.0%
ValueCountFrequency (%)
1 10
6.1%
30 2
 
1.2%
31 1
 
0.6%
32 1
 
0.6%
33 1
 
0.6%
35 1
 
0.6%
36 1
 
0.6%
37 1
 
0.6%
38 3
 
1.8%
40 2
 
1.2%
ValueCountFrequency (%)
81 1
 
0.6%
77 1
 
0.6%
76 2
1.2%
74 2
1.2%
73 2
1.2%
72 1
 
0.6%
71 2
1.2%
70 2
1.2%
69 4
2.5%
68 2
1.2%

QUIMICA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45
Distinct (%)31.0%
Missing18
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean51.672483
Minimum1
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-09-14T20:03:33.067288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q147
median55
Q362
95-th percentile71
Maximum76
Range75
Interquartile range (IQR)15

Descriptive statistics

Standard deviation16.838986
Coefficient of variation (CV)0.32587916
Kurtosis3.243516
Mean51.672483
Median Absolute Deviation (MAD)8
Skewness-1.7171194
Sum7492.51
Variance283.55143
MonotonicityNot monotonic
2023-09-14T20:03:33.246311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 10
 
6.1%
58 8
 
4.9%
56 7
 
4.3%
53 7
 
4.3%
60 7
 
4.3%
51 6
 
3.7%
55 6
 
3.7%
66 6
 
3.7%
52 6
 
3.7%
54 5
 
3.1%
Other values (35) 77
47.2%
(Missing) 18
 
11.0%
ValueCountFrequency (%)
1 10
6.1%
31 1
 
0.6%
33 1
 
0.6%
34 1
 
0.6%
36 1
 
0.6%
37 1
 
0.6%
37.51 1
 
0.6%
38 1
 
0.6%
39 3
 
1.8%
40 2
 
1.2%
ValueCountFrequency (%)
76 1
 
0.6%
75 1
 
0.6%
74 1
 
0.6%
72 3
1.8%
71 4
2.5%
70 2
 
1.2%
69 3
1.8%
68 3
1.8%
67 2
 
1.2%
66 6
3.7%

LENGUAJE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)30.6%
Missing19
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean54.926319
Minimum1
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-09-14T20:03:33.425056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q151
median58
Q365
95-th percentile74.7
Maximum83
Range82
Interquartile range (IQR)14

Descriptive statistics

Standard deviation17.501654
Coefficient of variation (CV)0.31863876
Kurtosis3.8664893
Mean54.926319
Median Absolute Deviation (MAD)7
Skewness-1.8796582
Sum7909.39
Variance306.3079
MonotonicityNot monotonic
2023-09-14T20:03:33.595468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 10
 
6.1%
63 9
 
5.5%
58 7
 
4.3%
57 7
 
4.3%
54 6
 
3.7%
70 6
 
3.7%
59 5
 
3.1%
66 5
 
3.1%
60 5
 
3.1%
55 5
 
3.1%
Other values (34) 79
48.5%
(Missing) 19
 
11.7%
ValueCountFrequency (%)
1 10
6.1%
31 1
 
0.6%
37 1
 
0.6%
39 1
 
0.6%
40 2
 
1.2%
41 2
 
1.2%
42 1
 
0.6%
43 1
 
0.6%
44 2
 
1.2%
45 1
 
0.6%
ValueCountFrequency (%)
83 1
0.6%
81 1
0.6%
80 1
0.6%
79 1
0.6%
78 1
0.6%
76 1
0.6%
75 2
1.2%
73 1
0.6%
72 2
1.2%
71 2
1.2%

GEOGRAFIA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct49
Distinct (%)33.8%
Missing18
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean51.612414
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-09-14T20:03:33.927613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q144
median56
Q363
95-th percentile71.8
Maximum81
Range80
Interquartile range (IQR)19

Descriptive statistics

Standard deviation17.682041
Coefficient of variation (CV)0.34259279
Kurtosis2.2438414
Mean51.612414
Median Absolute Deviation (MAD)10
Skewness-1.456458
Sum7483.8
Variance312.65457
MonotonicityNot monotonic
2023-09-14T20:03:34.114890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1 10
 
6.1%
59 7
 
4.3%
66 7
 
4.3%
63 6
 
3.7%
55 6
 
3.7%
57 6
 
3.7%
44 6
 
3.7%
58 5
 
3.1%
52 5
 
3.1%
67 5
 
3.1%
Other values (39) 82
50.3%
(Missing) 18
 
11.0%
ValueCountFrequency (%)
1 10
6.1%
30 2
 
1.2%
31 1
 
0.6%
32 1
 
0.6%
33 1
 
0.6%
35 1
 
0.6%
36 1
 
0.6%
36.8 1
 
0.6%
37 2
 
1.2%
38 3
 
1.8%
ValueCountFrequency (%)
81 1
 
0.6%
77 1
 
0.6%
76 1
 
0.6%
74 2
1.2%
73 2
1.2%
72 1
 
0.6%
71 3
1.8%
70 1
 
0.6%
69 4
2.5%
68 2
1.2%

IDIOMA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45
Distinct (%)31.2%
Missing19
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean55.283264
Minimum0
Maximum84
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-09-14T20:03:34.295583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q149
median57
Q368.25
95-th percentile78
Maximum84
Range84
Interquartile range (IQR)19.25

Descriptive statistics

Standard deviation18.934888
Coefficient of variation (CV)0.3425067
Kurtosis2.7750392
Mean55.283264
Median Absolute Deviation (MAD)9
Skewness-1.5971287
Sum7960.79
Variance358.53
MonotonicityNot monotonic
2023-09-14T20:03:34.464960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 10
 
6.1%
55 9
 
5.5%
70 7
 
4.3%
58 6
 
3.7%
57 6
 
3.7%
47 6
 
3.7%
71 6
 
3.7%
61 6
 
3.7%
52 5
 
3.1%
72 5
 
3.1%
Other values (35) 78
47.9%
(Missing) 19
 
11.7%
ValueCountFrequency (%)
0 1
 
0.6%
1 10
6.1%
31 1
 
0.6%
37 1
 
0.6%
39.79 1
 
0.6%
41 1
 
0.6%
42 3
 
1.8%
43 1
 
0.6%
44 2
 
1.2%
46 4
 
2.5%
ValueCountFrequency (%)
84 1
 
0.6%
83 1
 
0.6%
82 1
 
0.6%
80 2
 
1.2%
79 2
 
1.2%
78 2
 
1.2%
75 1
 
0.6%
74 4
2.5%
73 3
1.8%
72 5
3.1%

INTERDISCIPLINAR
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7
Distinct (%)26.9%
Missing137
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean20.024231
Minimum0
Maximum353
Zeros11
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-09-14T20:03:34.597068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile61.25
Maximum353
Range353
Interquartile range (IQR)1

Descriptive statistics

Standard deviation69.986313
Coefficient of variation (CV)3.4950812
Kurtosis22.686409
Mean20.024231
Median Absolute Deviation (MAD)1
Skewness4.6627337
Sum520.63
Variance4898.0841
MonotonicityNot monotonic
2023-09-14T20:03:34.729991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 11
 
6.7%
1 10
 
6.1%
4 1
 
0.6%
44 1
 
0.6%
67 1
 
0.6%
353 1
 
0.6%
42.63 1
 
0.6%
(Missing) 137
84.0%
ValueCountFrequency (%)
0 11
6.7%
1 10
6.1%
4 1
 
0.6%
42.63 1
 
0.6%
44 1
 
0.6%
67 1
 
0.6%
353 1
 
0.6%
ValueCountFrequency (%)
353 1
 
0.6%
67 1
 
0.6%
44 1
 
0.6%
42.63 1
 
0.6%
4 1
 
0.6%
1 10
6.1%
0 11
6.7%

Interactions

2023-09-14T20:03:28.260836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:13.108553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:14.636149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:16.091715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:17.501559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:18.825144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:20.106632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:21.404591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:22.945755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:24.313092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:25.589181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:26.963194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:28.367515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:13.211422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:14.757211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:16.208201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:17.611277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:18.928898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:20.210324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:21.517357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:23.057606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:24.418220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:25.702500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:27.073177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:28.483297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:13.329385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:14.887567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:16.341930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:17.733740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:19.046010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:20.328999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:21.641781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:23.180752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:24.534090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:25.826963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:27.192046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:28.599739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:13.442405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:15.016264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:16.466824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:17.848578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:19.157633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:20.443294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:21.918016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:23.304893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:24.645774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:25.945936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:27.303898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:28.715701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:13.547786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:15.136492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:16.578884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:17.955175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:19.259843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:20.545715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:22.030153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:23.411718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:24.746411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:26.055748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:27.404652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:28.998621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:13.649235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:15.248826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:16.682430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:18.053976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:19.353882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:20.644452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:22.135738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:23.517467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:24.843281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:26.162504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:27.502849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:29.113364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:13.753421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:15.364166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:16.791450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:18.158690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:19.453250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:20.745969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:22.246283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:23.625664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:24.942384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:26.271343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:27.602582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:29.227790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:13.878779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:15.491260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:16.909804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:18.274776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:19.567270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:20.863485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:22.368476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:23.748455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:25.057972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:26.394284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:27.721925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:29.342853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:14.004081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:15.616823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:17.032721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:18.389449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:19.680374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:20.975648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:22.487155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:23.865742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:25.168488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:26.514453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:27.834036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:29.459183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:14.108010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:15.725109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:17.137010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:18.487044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:19.777656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:21.074903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:22.594079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:23.970882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:25.266442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:26.618314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:27.930331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:29.575085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:14.233835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:15.851695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:17.267684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:18.604633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:19.894405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:21.191145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:22.717588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:24.091023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:25.379361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:26.739637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:28.043655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:29.694649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:14.341339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:15.964105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:17.383188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:18.702686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:19.988608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:21.289733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:22.827859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:24.197056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:25.473545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:26.846150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T20:03:28.141258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-14T20:03:34.846506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ID_ESTUDIANTEICFES_ANTIGUOBIOLOGIAMATEMATICAFILOSOFIAFISICAHISTORIAQUIMICALENGUAJEGEOGRAFIAIDIOMAINTERDISCIPLINAR
ID_ESTUDIANTE1.000-0.147-0.288-0.198-0.222-0.228-0.224-0.279-0.173-0.210-0.1380.127
ICFES_ANTIGUO-0.1471.0000.9110.8800.9190.8650.9450.9090.8990.9320.784-0.514
BIOLOGIA-0.2880.9111.0000.7540.7750.7990.8040.9920.7340.7960.665-0.085
MATEMATICA-0.1980.8800.7541.0000.7300.9000.7620.7550.7090.7570.716-0.258
FILOSOFIA-0.2220.9190.7750.7301.0000.7500.8270.7680.9620.8200.623-0.120
FISICA-0.2280.8650.7990.9000.7501.0000.7470.7940.7240.7230.683-0.170
HISTORIA-0.2240.9450.8040.7620.8270.7471.0000.7990.7940.9840.639-0.129
QUIMICA-0.2790.9090.9920.7550.7680.7940.7991.0000.7280.8040.660-0.123
LENGUAJE-0.1730.8990.7340.7090.9620.7240.7940.7281.0000.7900.623-0.117
GEOGRAFIA-0.2100.9320.7960.7570.8200.7230.9840.8040.7901.0000.627-0.167
IDIOMA-0.1380.7840.6650.7160.6230.6830.6390.6600.6230.6271.000-0.454
INTERDISCIPLINAR0.127-0.514-0.085-0.258-0.120-0.170-0.129-0.123-0.117-0.167-0.4541.000

Missing values

2023-09-14T20:03:29.864503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-14T20:03:30.094592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-14T20:03:30.303205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ID_ESTUDIANTEICFES_ANTIGUOBIOLOGIAMATEMATICAFILOSOFIAFISICAHISTORIAQUIMICALENGUAJEGEOGRAFIAIDIOMAINTERDISCIPLINAR
02236077280.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
12236078349.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
22236079340.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32220846344.069.072.066.072.066.069.067.066.073.0NaN
42220847304.066.061.058.061.058.066.058.058.062.0NaN
52220848284.054.056.062.056.056.054.062.056.055.0NaN
62185320NaN50.058.054.050.063.050.054.063.061.0NaN
72185384NaN72.082.050.072.069.072.050.069.080.0NaN
82185526NaN62.066.062.062.059.062.069.059.042.04.0
92185527NaN55.039.055.055.055.055.051.055.044.044.0
ID_ESTUDIANTEICFES_ANTIGUOBIOLOGIAMATEMATICAFILOSOFIAFISICAHISTORIAQUIMICALENGUAJEGEOGRAFIAIDIOMAINTERDISCIPLINAR
1532185300NaN58.058.064.058.059.058.064.059.072.0NaN
1542185512288.062.061.057.062.057.062.053.057.048.0NaN
1552185265NaN53.062.052.053.060.053.052.060.056.0NaN
1562186045NaN66.069.072.066.061.066.072.061.070.0NaN
1572185619NaN71.073.063.071.063.071.063.063.070.0NaN
1582186315NaN45.050.060.045.054.045.060.054.048.0NaN
1592185175NaN58.061.066.058.067.058.066.067.047.0NaN
1602186323NaN47.057.058.047.055.047.058.055.049.0NaN
1612186063NaN65.058.058.065.040.065.058.040.060.0NaN
1622185181NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN